Optimal RF-Chain Selection with Hybrid Analog and Digital Beamformer Design Using Deep Learning in mmWave Communication System

Optimal RF-Chain Selection with Hybrid Analog and Digital Beamformer Design Using Deep Learning in mmWave Communication System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimal RF-Chain Selection with Hybrid Analog and Digital Beamformer Design Using Deep Learning in mmWave Communication System C Shekhar Kotikalapudi, Jeyakumar P, Divya Sri Kamjula, B Sanjai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3237063/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Energy efficiency is treated as an important performance metric in wireless communication systems, and selecting the appropriate RF chains plays a vital role in maximizing overall system performance, energy efficiency , and spectral efficiency. To maximize energy efficiency and spectral efficiency, a two-level optimization technique for RF chain selection and beamforming is proposed in this paper. In the first level, RF chain selection is performed using particle swarm optimization (PSO) algorithm to maximize system rate. The selected RF chains are then used in the second level, where a two-stage deep learning-based Beamform-ing Neural Network (BFNN) is employed to optimize the beamforming process while being robust to imperfect channel state information (CSI). The BFNN takes the estimated CSI as input and outputs the optimized analog beamformer. The proposed BFNN is trained using a loss function tailored for beamforming optimization. Experimental results validate the effectiveness of the proposed method, showcasing significant improvements in spectral efficiency, while mitigating the effects of imperfect CSI. The spectral efficiency with and without RF selection using PSO is 51.5439 and 47.7258 respectively at SNR of 20dB. This approach provides an efficient and robust solution for RF chain selection and beamforming optimization in wireless communication systems. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3237063","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":224774038,"identity":"925096ae-3f84-4716-97ab-8b75aa01f0ac","order_by":0,"name":"C Shekhar Kotikalapudi","email":"data:image/png;base64,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","orcid":"","institution":"National Institute of Technology Tiruchirappalli","correspondingAuthor":true,"prefix":"","firstName":"C","middleName":"Shekhar","lastName":"Kotikalapudi","suffix":""},{"id":224774039,"identity":"1edc7a12-4018-4943-8600-9dd0fba0afaf","order_by":1,"name":"Jeyakumar P","email":"","orcid":"","institution":"M Kumarasamy College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Jeyakumar","middleName":"","lastName":"P","suffix":""},{"id":224774040,"identity":"66104f1c-051d-4b70-ab15-51ddb8ed347d","order_by":2,"name":"Divya Sri Kamjula","email":"","orcid":"","institution":"National Institute of Technology Tiruchirappalli","correspondingAuthor":false,"prefix":"","firstName":"Divya","middleName":"Sri","lastName":"Kamjula","suffix":""},{"id":224774041,"identity":"f2f1b164-79ea-4f02-b47c-ef0a0779653d","order_by":3,"name":"B Sanjai","email":"","orcid":"","institution":"M Kumarasamy College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"B","middleName":"","lastName":"Sanjai","suffix":""},{"id":224774042,"identity":"cff70b6c-3f76-40f5-8fcd-e66b043b62d3","order_by":4,"name":"P Muthuchidambaranathan","email":"","orcid":"","institution":"National Institute of Technology Tiruchirappalli","correspondingAuthor":false,"prefix":"","firstName":"P","middleName":"","lastName":"Muthuchidambaranathan","suffix":""}],"badges":[],"createdAt":"2023-08-05 10:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3237063/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3237063/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46404537,"identity":"d5f7ed0c-3d4a-48e8-8016-24b162fa2574","added_by":"auto","created_at":"2023-11-14 09:53:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":841048,"visible":true,"origin":"","legend":"","description":"","filename":"ChandrasekarWork1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3237063/v1_covered_c83d371f-bc7e-4785-980b-7ca4f5b1c61c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eOptimal RF-Chain Selection with Hybrid Analog and Digital Beamformer Design Using Deep Learning in mmWave Communication System\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3237063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3237063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnergy efficiency is treated as an important performance metric in wireless communication systems, and selecting the appropriate RF chains plays a vital role in maximizing overall system performance, energy efficiency , and spectral efficiency. To maximize energy efficiency and spectral efficiency, a two-level optimization technique for RF chain selection and beamforming is proposed in this paper. In the first level, RF chain selection is performed using particle swarm optimization (PSO) algorithm to maximize system rate. The selected RF chains are then used in the second level, where a two-stage deep learning-based Beamform-ing Neural Network (BFNN) is employed to optimize the beamforming process while being robust to imperfect channel state information (CSI). The BFNN takes the estimated CSI as input and outputs the optimized analog beamformer. The proposed BFNN is trained using a loss function tailored for beamforming optimization. Experimental results validate the effectiveness of the proposed method, showcasing significant improvements in spectral efficiency, while mitigating the effects of imperfect CSI. The spectral efficiency with and without RF selection using PSO is 51.5439 and 47.7258 respectively at SNR of 20dB. 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